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app (1).py

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+
+import gradio as gr
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+from sklearn.metrics.pairwise import cosine_similarity
+from PIL import Image
+
+# טעינת המודל והנתונים
+print("⏳ Loading Model and Data...")
+model = SentenceTransformer('clip-ViT-B-32')
+df = pd.read_parquet("ven_inventory.parquet")
+inventory_embeddings = np.stack(df['embedding'].values)
+
+# טעינת התמונות (דורש חיבור אינטרנט ב-Space)
+dataset = load_dataset("detection-datasets/fashionpedia", split='train')
+subset = dataset.select(range(5050))
+
+def recommend(input_data, input_mode):
+    # המרת קלט לוקטור
+    if input_mode == "Text":
+        query_emb = model.encode([input_data])
+    else:
+        img = Image.fromarray(input_data).convert("RGB")
+        query_emb = model.encode([img])
+    
+    query_emb = query_emb / np.linalg.norm(query_emb)
+    
+    # חישוב דמיון
+    scores = cosine_similarity(query_emb, inventory_embeddings)[0]
+    top_indices = np.argsort(scores)[::-1][:3]
+    
+    results = []
+    for idx in top_indices:
+        actual_idx = int(idx)
+        results.append((
+            subset[actual_idx]['image'], 
+            f"Match Score: {scores[actual_idx]:.2%} | Cluster: {df.iloc[actual_idx]['cluster']}"
+        ))
+    return results
+
+# ממשק Gradio
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🌿 Ven Community - Fashion Recommender")
+    gr.Markdown("Search Ven's inventory by text or image.")
+    
+    with gr.Row():
+        with gr.Column():
+            mode = gr.Radio(["Text", "Image"], label="Input Type", value="Text")
+            txt = gr.Textbox(label="Description", visible=True)
+            img = gr.Image(label="Upload Image", visible=False)
+            btn = gr.Button("Find Similar Items", variant="primary")
+        
+        with gr.Column():
+            gallery = gr.Gallery(label="Results", columns=3)
+
+    mode.change(lambda m: (gr.update(visible=m=="Text"), gr.update(visible=m=="Image")), 
+                inputs=mode, outputs=[txt, img])
+    
+    btn.click(fn=recommend, inputs=[txt if mode.value=="Text" else img, mode], outputs=gallery)
+
+demo.launch()

requirements (1).txt

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+
+gradio
+pandas
+numpy
+torch
+sentence-transformers
+pillow
+pyarrow
+datasets
+scikit-learn

ven_inventory.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ab570a31e9f2d9ad4241487009b95b5c07d971737788ca065bc0647e3298ba1
+size 11001565